Integrated modeling of human-earth system interactions: An application of GCAM-fusion
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Engineering topics
Publications and source records attributed to Link, Robert.
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Abstract Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.
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gcamland is an open source R package that was built to allocate land across a variety of types based on changes in agricultural yield and commodity price. The land allocation algorithm is based on the one included in the Global Change Assessment Model (GCAM). gcamland includes the ability to run in a historical mode, enabling model validation and parameter estimation, or in a future mode, simulating changes in land use/land cover in the future. For both modes, gcamland can run a single simulation or a large ensemble of simulations with different parameters. When ensembles are generated in the historical mode, gcamland calculates the likelihood of a given parameter set by comparing to observational data. gcamland is publicly available via GitHub and has can be adjusted to represent alternative scenarios or configured to different regions and land types.
Earth System Models (ESMs) are excellent tools for quantifying many aspects of future climate dynamics but are too computationally expensive to produce large collections of scenarios for downstream users of ESM data. In particular, many researchers focused on the impacts of climate change require large collections of ESM runs to rigorously study the impacts to both human and natural systems of low-frequency high-importance events, such as multi-year droughts. Climate model emulators provide an effective mechanism for filling this gap, reproducing many aspects of ESMs rapidly but with lower precision. The fldgen v1.0 R package quickly generates thousands of realizations of gridded temperature fields by randomizing the residuals of pattern scaling temperature output from any single ESM, retaining the spatial and temporal variance and covariance structures of the input data at a low computational cost. The fldgen v2.0 R package described here extends this capability to produce joint realizations of multiple variables, with a focus on temperature and precipitation in an open source software package available for community use (https://github.com/jgcri/fldgen). This substantially improves the fldgen package by removing the requirement that the ESM variables be normally distributed, and will enable researchers to quickly generate covarying temperature and precipitation data that are synthetic but faithful to the characteristics of the original ESM.
Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We introduce the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.
The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility.
Generate random global temperature fields with spatial and temporal correlation derived from an ESM As of version 2.0, fldgen allows to emulate any climate variable, not just ones that are approximately normally distributed, and it allows us to emulate climate variables in combinations, such as temperature and precipitation. These new capabilities can be used for risk modeling in areas such as drought and it agricultural impacts, where both temperature and moisture play a role.
Generate random global temperature fields with spatial and temporal correlation derived from an ESM
This paper explores potential future implications of climate change on building energy expenditures around the globe. Increasing expenditures result from increased electricity use for cooling, and are offset to varying degrees, depending on the region, by decreased energy consumption for heating. The analysis is conducted using a model of the global buildings sector within the GCAM integrated assessment model. The integrated assessment framework is valuable because it represents socioeconomic and energy system changes that will be important for understanding building energy expenditures in the future. Results indicate that changes in net expenditures are not uniform across the globe. Net expenditures decrease in some regions, such as Canada and Russia, where heating demands currently dominate, and increase the most in areas with less demand for space heating and greater demand for space cooling. We explain these results in terms of the basic drivers that link building energy expenditures to regional climate.